class_docstr: Given wrapped example, e.g. A fun movie ! it is <mask> and <mask> .
  The input tokens is:  A fun movie ! it is <s_0> and <s_1> . <eod>
  The decoder tokens is: <s> <s_0> <s_1> <eod>
  The expected output is: <s_0> good <s_1> interesting </s>
  Another solution is add verbalizer label words into the labels, thus generating
  'interesting' given 'good'. However, it is not implemented in this version.
code: def truncate_decoder_inputs(self, inputs):
      if self.decode_from_start:
          inputs["decoder_input_ids"].insert(
              0, self.tokenizer.sod_token_id
          )
          inputs["loss_ids"].insert(0, 0)
  
      for key in inputs:
          inputs[key] = inputs[key][: self.decoder_max_length - 1]
  
      if self.predict_eos:
          inputs["decoder_input_ids"].append(
              self.tokenizer.eos_token_id
          )
          inputs["loss_ids"].append(1)
      inputs = self.padding(
          inputs,
          max_len=self.decoder_max_length,
          pad_id_for_inputs=self.tokenizer.pad_token_id,
      )
      return inputs
  
imports: from json import decoder
  from transformers.tokenization_utils import PreTrainedTokenizer
  from openprompt.plms.utils import TokenizerWrapper
  from typing import List, Dict, Optional
  from collections import defaultdict
  from openprompt.utils.logging import logger
label_desc: Use image node_id 6 for calling the CPM2TokenizerWrapper obj's underlying member method code with example usage: obj.truncate_decoder_inputs(inputs) and returns: inputs